A Breif Review on Data-driven Battery Health Estimation Methods for Energy Storage Systems

Abstract

Battery degradation has an impact on the safety and sustain ability of energy storage systems, which is a consequence of multiple coupled ageing mechanisms. The caused factors include battery chemistry and manufacturing, as well as environmental and operating conditions. Hence, the ageing mechanisms are highly complicated to characterize. The state of health (SOH) of a battery is a commonly used metric to evaluate its aging level. Monitoring battery SOH can realize safety and reliable operation of battery management systems. Data-driven methods for battery health estimation and prediction are gaining increasing attention in both academia and industry due to the advantage of avoiding complex physical models. Hence, this paper reviews current state-of-the-art data-driven SOH estimation methods published in 2018–2022, where the variants and extensions of each method existing in current papers are introduced. Finally, the current faced challenges and the solutions are analyzed.